System and method for assessing power transformers

ABSTRACT

A system and method for evaluating power transformers is disclosed. The method includes the steps of acquiring data representing one or more parameters of a power transformer, using rules to derive one or more broad physical conditions of the power transformer from the acquired data, and using the broad physical conditions as inputs to compute a plurality of indices. Each index represents a category of failure mechanisms of the power transformer. The method further includes the steps of using the plurality of indices to determine a corrective action and performing the corrective action on the power transformer.

BACKGROUND OF THE INVENTION

The field of power transformer diagnostics is characterized by inexactreasoning and reasoning under uncertainty. Lack of information on theconstruction and design of a transformer, limited test opportunities,uncertain operating conditions and a lack of empirical data combine fora task that is far from an exact science. Interpretation of test resultsis more of an art than a science. Practitioners must examine sparseevidence from a variety of sources and make an educated guess as to thepresence of a potential fault condition, the severity of that faultcondition and the appropriate corrective action (which often is limitedto costly replacement).

Unfortunately, in today's industry environment, these same activitiesmust be performed with reduced manpower, reduced domain expertise and anever increasing amount of raw data. In addition, asset management andmaintenance decision are coming under increased scrutiny, both fromwithin the company as pressures to constrain costs mount, and fromexternal regulators. Transformers are a key component in power delivery.Transformer failures can cost millions of dollars in consequential costsbesides leading to other system reliability and operational challenges.

Accordingly, there is a clear need for an established, defensible,transparent and repeatable condition assessment methodology.

BRIEF SUMMARY OF THE INVENTION

These and other shortcomings of the prior art are addressed by thepresent invention, which provides an improved diagnosis and betterunderstanding of transformer condition to allow for operation andmaintenance decisions and replacement decisions with a holisticmethodology that utilize sophisticated algorithms based on the knowledgeof transformer design, construction and failure mechanisms. Themethodology enables a consistent, systematic, repeatable and documentedprocess.

According to one aspect of the present invention, a method forevaluating and diagnosing a condition of a power transformer containedin a power transmission system includes the steps of acquiring datarepresenting one or more parameters of a power transformer, using rulesto derive one or more broad physical conditions of the power transformerfrom the acquired data, and using the broad physical conditions asinputs to compute a plurality of indices. Each index represents acategory of failure mechanisms of the power transformer. The methodfurther including the steps of using the plurality of indices todetermine a corrective action, and performing the corrective action onthe power transformer.

According to another aspect of the present invention, a systemconfigured to evaluate and diagnose a condition of a power transformercontained in a power transmission system includes a computing deviceconfigured to provide a user interface to allow a user to input data andexecute rules to analyze the data, and a plurality of modules executedby the computing device, the modules being configured to conduct variousstages of analysis on one or more power transformers. The modulesinclude an input module executed by the computing device to allow a userto input data gathered for each power transformer being analyzed, ananalysis engine module executed by the computing device in response tothe data being entered into the system, the analysis engine executingpre-defined rules to determine indices, and an output module executed bythe computing device in response to the analysis engine moduledetermining indices. The output module displays results of the analysisengine module for a user to view and prompts the user to perform anaction representative of a value assigned to the indices.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter that is regarded as the invention may be bestunderstood by reference to the following description taken inconjunction with the accompanying drawing figures in which:

FIG. 1 is a screen shot of a graphical user interface of the systemaccording to an embodiment of the invention;

FIGS. 2-6 are flow charts of the system and method according to anembodiment of the invention;

FIG. 7 shows combustible gas generation v. approximate oil decompositiontemperature;

FIG. 8 is a graphical representation of a belief according to the systemand method of the current invention;

FIG. 9 is a flow chart of the method for evaluating a transformer; and

FIGS. 10 and 11 show flow charts for oil and end of life evaluation.

DETAILED DESCRIPTION OF THE INVENTION

A system and method according to an embodiment of the invention is showngenerally in FIGS. 1-6 at reference numeral 10. The system and methoduses hardware and software to provide a rule-based expert system capableof performing transformer diagnosis. One example of a rule-based expertsystem is MYCIN. MYCIN is a rule-based inference engine developed byStanford University for the medical field to evaluate hypothesis. In thecase of transformer diagnostics, the hypotheses that are to be evaluatedare whether a given failure mode or failure mechanism is present.Evidence, in the form of test data and nameplate information, isevaluated by rules to determine a “belief” that a given failuremechanism is present.

For purposes of this discussion, the system and method of the currentinvention will be discussed with respect to the MYCIN rule-basedinference engine; however, it should be appreciated that the currentinvention is not limited to MYCIN and may be based off of other suitableplatforms.

Before discussing the current invention in detail, it is important toprovide a general understanding of rule-based expert systems such asMYCIN. To overcome some of the perceived limitations of a rigorousprobabilistic analysis, the designers of the MYCIN system proposed anapproach that allows the domain expert, providing the knowledge, toexpress uncertainty in a natural fashion without the restrictions ofrigorous probability theory. Central to this approach was thequantitative expression of confirmation (or alternativelydisconfirmation). Confirmation could be defined as the degree to which apiece of evidence confirms a hypothesis. Confirmation differs fromprobability in some key aspects. Foremost is the notion thatconfirmation in a hypothesis does not necessarily disconfirm thenegation of the hypothesis. In other words, confirmation anddisconfirmation are separate and must be dealt with differently.

To address this, MYCIN introduced two new quantities, termed belief (MB)and disbelief (MD). MB[h,e] is the measure of increased belief in thehypothesis, h, based on the evidence, e. MD[h,e] is the measure ofdecreased belief in the hypothesis, h, based on the evidence, e. Thesequantities are expressed as numbers in the range 0 to 1, with highervalues indicating greater degree of belief or disbelief. Values equal to1.0 express certain belief or disbelief in a hypothesis. These functionsare not to be treated as probabilities, but can be expressed in terms ofprobability as follows:

$\begin{matrix}{{{MB}\left\lbrack {h,e} \right\rbrack} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} {P(h)}} = 1} \\\frac{{\max \left\lbrack {{P\left( {he} \right)},{P(h)}} \right\rbrack} - {P(h)}}{1 - {P(h)}} & {otherwise}\end{matrix} \right.} & (1.1) \\{{{MD}\left\lbrack {h,e} \right\rbrack} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} {P(h)}} = 0} \\\frac{{\min \left\lbrack {{P\left( {he} \right)},{P(h)}} \right\rbrack} - {P(h)}}{- {P(h)}} & {otherwise}\end{matrix} \right.} & (1.1)\end{matrix}$

From the above, it is possible to see that, although different thanprobability, the measures of belief and disbelief do have somemathematical foundation. The measure of belief is approximately equal tothe conditional probability in instances where there are a large numberof mutually exclusive possibilities (small prior probability P(h)).

For the sake of convenience, a third quantity, referred to as the“certainty factor”, was developed to provide a convenient way to expressthe combined measures of belief and disbelief. Originally, the certaintyfactor was simply the difference between the total measure of belief anddisbelief (MB-MD). However, this led to some difficulties in certainscenarios. To resolve these problems, the certainty factor wasre-defined as follows:

$\begin{matrix}{{CF} = \frac{{MB} - {MD}}{1 - {\min \left( {{MB},{MD}} \right)}}} & (1.2)\end{matrix}$

With this redefinition, it is possible to drop the notion of belief anddisbelief when convenient and focus on the certainty factor as thecentral measure of belief. However, this is only advantageous insimplifying computations and reducing data storage requirements. Indeveloping the knowledge base, there is some conceptual benefit tomaintain a distinction between belief and disbelief. Some of the keycharacteristics of the certainty factor are given in the table below.

Characteristics Values Ranges 0 ≦ MB ≦ 1 0 ≦ MD ≦ 1 −1 ≦ CF ≦ 1 CertainTrue Hypothesis MB = 1 P(H|E) = 1 MD = 0 CF = 1 Certain False HypothesisMB = 0 P(H′|E) = 1 MD = 1 CF = −1 Lack of Evidence MB = 0 P(H|E) = P(H)MD = 0 CF = 0

When interpreting certainty factors, it is critical to keep in mind thatcertainty factors are not probabilities. Aside from the obviousobservation that beliefs range from −1 to +1, where probabilitiestypically range from 0 to 1, there are some minor, subtle differences.For example, take a set of four mutually exclusive and exhaustiveoutcomes, where one and only one outcome can be true. In the absence ofany information, Bayesian probability might assign an equal probabilityto each of 0.25, or perhaps allocate some statistically-based priorprobabilities that total 1.0. However, the certainty factor or belieffor each outcome would, by the definition of a certainty factor, be zerosince there is no evidence to support a higher or lower belief. Whereasin traditional probability theory, the sum of the probabilities for theset of all possibilities must equal 1, this restriction is not presentin certainty factor theory.

Another significant difference between probability theory and certaintyfactors concerns the negation of a hypothesis. In traditionalprobability theory, the probability of the negation of a hypothesis isequal to one minus the probability of the hypothesis, ie. P(

h)=1−P(h). In certainty factor theory, however, this is not true. Thecertainty factor in the negation of a hypothesis is equal to thenegative of the certainty factor in a hypothesis, ie. CF(

h)=−CF(h). In terms of the present discussion, if there is a +0.2 beliefin “lead heating” being present, then there is a −0.2 belief in “leadheating” NOT being present.

Having developed a suitable means for expressing belief and disbelief ina hypothesis given a single piece of evidence, there must be a means forcombining the beliefs contributed by multiple pieces of evidence toarrive at a representative belief in a given hypothesis or conclusion.This combining function should obey several properties. First, it shouldbe independent of order, ie. A⊕(B⊕C)=(A⊕B)⊕C. In addition, the combiningfunction should follow the logic of the certainty factors. If twoindicators support a given hypothesis, the combined belief in thathypothesis should be higher than the belief from each indicator takenindividually. Similarly, if two beliefs support the negation of ahypothesis (e.g. that a given alternative is not attractive), then thecombined belief should be more negative than each individual belief.Finally, in the case of conflicting, the combined belief should becloser to 0, or “unknown”.

The summation method utilized in the MYCIN program has all of theproperties outlined above. This summation method, here called the “MYCINSum”, is a mathematical expression of the idea that two beliefs with thesame conclusion will reinforce each other, while opposite beliefs willreduce certainty (move the certainty factor toward 0) in a certainoutcome.

$\begin{matrix}{{{CF}_{COMBINE}\left( {X,Y} \right)} = \left\{ \begin{matrix}{X + {Y\left( {1 - X} \right)}} & {X,{{Y\mspace{14mu} {both}} > 0}} \\\frac{X + Y}{1 - {\min \left( {{X},{Y}} \right)}} & {{{one}\mspace{14mu} {of}\mspace{14mu} X},{Y < 0}} \\{X + {Y\left( {1 + X} \right)}} & {X,{{Y\mspace{14mu} {both}} < 0}}\end{matrix} \right.} & (1.3)\end{matrix}$

When combining beliefs, if both beliefs are consistent, they willreinforce the combined belief in a given conclusion. For example, ifthere are two test results that both suggest a given transformer mightbe “aged”, then there is greater confidence that the unit is in factaged. However, if one test suggests an aged transformer and anothersuggests a serviceable transformer, then the combination of the twoleaves the practitioner with an increased level of uncertainty.

The key advantage of certainty factors is that they allow theconsideration of multiple pieces of evidence without needing largenumbers of prior or conditional probabilities. In an oversimplifiedrespect, certainty factors assume all evidence is conditionallyindependent, and can therefore be represented simply as a series ofrules independent of order. Whereas Bayesian belief networks or othermore mathematically rigorous methods require more structure than simplerules allow, MYCIN certainty factors allow for a rule-based system thatcan be developed in an evolutionary process, with each ruleencapsulating a single “nugget” of knowledge, versus other systems wherestructure and ontology must be carefully designed in a fullypremeditated manner.

Referring to FIGS. 1-6, in general, the system 10 includes softwaredisposed on computer readable media and a computing device or processor14 to process data and perform pre-determined functions. The softwareprovides an input module 11, an analysis engine module 12, an outputmodule 13, and a graphical user interface (GUI) 16 to allow a user toaccess the input module 11 and allow a user to input data representativeof a particular transformer, such as name plate information, inspectiondata, etc. The analysis engine module 12 (described in more detailbelow) uses the processer 14 and pre-programmed expert rules to analyzethe data, and the output module 13 provides a user with results from theanalysis engine module 12 so that the user can take an appropriateaction. It should be appreciated that the method may be performedwithout the hardware and software.

Examples of input data include:

-   -   Components—Components are the top-level objects. They represent        the physical systems or subsystems being diagnosed. The most        obvious component is the “Transformer” component. Components can        contain subcomponents, e.g. bushings or load tap changers.        Components are also the containers for information, in the form        of Activities. At a high level, containers also contain Facts in        the form of Failure Mechanisms or Actions (see section on Facts)    -   Activities—Facts are generally organized into Activities, with        the exception of unique component level facts, such as Failure        Mechanisms and Actions. An Activity acts as a template of facts        at a given instance in time. There may be several historical        instances of activities in a given “history”, from which trends        are generated. In addition, a rule-based diagnosis is performed        at each historical activity instance (point-in-time). Each        component also has associated with it a Description Activity        (only one instance) that provides facts that describe the        component. These are basic facts like “Manufacturer” or “HV        Voltage” that do not change with time.    -   Facts—A fact is a kernel of knowledge or a single piece of        factual information that can be used by the inference engine        rules to reason about other facts. Some of the facts are “input        facts” as given by the user. Other facts are inferred from the        rules. There are several fact types that include:        -   Text facts—Facts representing a string        -   Numeric facts—Facts representing numeric values, generally            continuous        -   List facts—Facts that can take on a discrete number of known            values (e.g. Manufacturer). A value is also included for            “Unknown”, where the given value is unknown in the rule            base.        -   Boolean Facts—A fact that takes on a Boolean yes/no or            true/false value. This is the most generic type of fact        -   Failure mechanism—An extension of a Boolean Fact that            indicates whether a given failure mechanism is present (true            or yes). This fact type is generally placed at the component            level.        -   Action—Also an extension of a Boolean Fact that indicates            whether a given Action should be undertaken (true or yes).            This fact type is also generally placed at the component            level.    -   Each fact, along with a discrete answer, has associated with it        a Belief, i.e., a number between −1.0 and +1.0, see FIG. 8, that        indicates the certainty one has in the value of the fact. It is        used in the rules to determine the weight a given fact has in        the evaluation of that rule.

The analysis engine module evaluates rules, as outlined above. At eachevaluation point, the analysis engine module iterates through all of therules for the given component and evaluates each rule at that point inthe component history. Rules follow a specific format and are expressedin a human-readable text format with a defined lexicon.

The rules are structured with two parts, a premise (If-part) and aconclusion (Then-part) and alternative (Else-part). As with allrule-based expert systems, the premise is first evaluated. If thepremise evaluates to true (a certainty factor greater than a nominalthreshold), then the conclusion is “fired” or executed. A certaintyfactor can be assigned to both the premise and the conclusion. The totalcertainty factor for the rule is the product of the premise and theconclusion.

By way of illustration, below is a typical rule:

IF Transformer.DGA.Ethylene = HIGH (50, 250) THENTransformer.DGA.blfHighTemp (0.4) ELSE!Transformer.DGA.blfHighTemperature (0.2) END “IF Ethylene is High THENthere is high temperature heating (T3) with a certainty factor (CF) of0.4 ELSE there is NOT high temperature heating (T3) with a certaintyfactor (CF) of 0.2).”

The premise “Ethylene is high” can be assigned a certainty factor toexpress the observer's confidence that the level of Ethylene is indeed“high”. For example, based on the value of Ethylene, the observer mightassign a belief of 0.6 that Ethylene is “high”. When a certainty factoris assigned to the premise, this is referred to as the “tally”. Thecertainty factor given in the conclusion of the rule represents the fullamount of belief that would be assigned to the hypothesis if the premisewere certain (tally equal to 1.0).

When the premise is a Boolean value, as is the case when observations orfacts have a yes/no value or are a discrete list of possibilities, aseparate certainty factor for the premise might seem unnecessary.However, when the indicator is numeric or is defined by a continuousrange rather than discrete values, the value of the indicator bears somerelation to the strength of the indication. The “tally” is a usefulmechanism for relating the strength of the indication to the conclusionof the rule (diagnosis or hypothesis).

In the case of numeric indicators, the numeric value of the indicator isassigned a certainty factor via the use of some function. This functioncan take any form, but presently consists of a simple sigmoid functionbetween “good” and “bad” thresholds. As an example, consider theindicator “Ethylene”. If the hypothesis being evaluated is “hightemperature heating (T3)”, then a higher Ethylene content would supportthis hypothesis. Rather than defining a single threshold value forEthylene, where all Ethylene concentrations below the threshold areassumed to be “good” (belief of −1.0) and those above are “bad” (beliefof +1.0), a function can be defined.

The S-Function for HIGH is defined as:

−1, x ≤ α${1 - {4\left( \frac{x - \alpha}{\gamma - \alpha} \right)^{2}}},{\alpha < x \leq {\beta  - 1 + {4\left( \frac{x - \gamma}{\gamma - \alpha} \right)^{2}}}},{\beta < x \leq \gamma}$1, x > γ.

To reduce inputs, beta defaults to alpha+gamma/2. This is purely forconvenience. The LOW function is simply the negation of the HIGHfunction, such that increasing values of the underlying numeric factvalue decrease the belief that the value is LOW. A third function,MODERATE, is also defined to allow for interpretation of values that mayhave diagnostic value over a range, but not above or below it.

The conclusion of a rule only is applied, or “fires”, when the belief inthe premise exceeds a given threshold. In the case of negative beliefs,the rule would not generally fire. In the example rule above, however, asecond conclusion was given as part of an “else” clause. The “else”clause allows the expression of disconfirming evidence, in this case lowvalues of Ethylene. Recall that belief and disbelief are conceptuallytwo different actions. Therefore, rules disconfirming a hypothesisshould be made explicit.

The premise of the rules may contain certain elementary logicalexpression such as “And”, “Or” and “Not”. These logical operations aredefined as shown in the table below. Note that the logical combinationof evidence in the premise is a different activity than the combinationof distinct, independent rules.

Expression Definition E₁ AND E₂ min[CF(H|E₁), CF(H|E₂)] E₁ OR E₂max[CF(H|E₁), CF(H|E₂)] NOT E₁ −CF(H|E₁)

While the appeal of the system is the independent nature of theindividual rules, allowing for the easy addition of knowledge, there aresome restrictions that should be followed to maintain a coherent ruleset:

-   -   1. Given mutually exclusive hypotheses for an observation, the        sum of their certainty factors (CFs) should not exceed 1.    -   2. Dependent pieces of evidence should be grouped into single        rather than multiple rules.    -   3. Care must be taken to avoid conflicting rules, i.e., a rule        that states a hypothesis is definitely true and a rule that        states the same hypothesis is definitely false. Absolute        certainty in rules must be used carefully.

In the case of a decision support system for transformer diagnostics,the hypotheses that are to be evaluated are whether a given failure modeor failure mechanism is present. Evidence, in the form of test data andnameplate information, is evaluated by rules such as those describedabove to determine a “belief” that a given failure mechanism is present.

The general overall evaluation process is as follows:

-   -   Diagnose subcomponents    -   Compile list of historical activity dates to serve as discrete        assessment points    -   For each date in the list above:        -   For each rule in the component rule base            -   Evaluate Rule Predicate            -   If Rule Predicate is greater than threshold, “fire” rule                -   If Rule is an Assignment Rule:                -    process fact assignment assigning a Belief to the                    assigned facts equal to the Rule Predicate Belief.                -   Else:                -    If Rule Predicate Belief is above rule “fire”                    threshold:                -    Process True Belief Assignment List                -    Else If Rule Predicate Belief is below rule “fire”                    threshold:                -    Process False Belief Assignment List        -   State Mechanism Beliefs        -   Evaluate Normal Degradation Increment    -   Compute Normal Degradation Index

There can be any number of failure mechanisms, arranged in any fashionthat makes physical sense. New failure mechanisms can be added to therule base at any time, without the need to adjust prior rules. Failuremechanisms can be as broad as “thermal” or as fine-grained as “windinginsulation deterioration—thermal degradation”. Fine-grainedclassifications provide more information to the user, but require morerules (and more evidence) to differentiate between the individualfailure mechanisms. The underlying diagnostic method must also becapable of differentiating between possible failure mechanisms.

In addition to failure mechanisms, the system approach can be applied toother “hypotheses”, such as whether or not a given action should beperformed. The rule-based approach allows additional hypotheses to beadded to the knowledge base simply by adding additional rules. In thisfashion, a series of actions or decisions can be assessed to give usersguidance on possible maintenance or testing that should be performedgiven the available information. For example, a rule might be defined tosuggest oil reclamation or replacement if the oil quality drops belowthresholds. As another example, a rule could be defined that wouldsuggest power factor testing if moisture-in-oil indicates thepossibility of high moisture.

These hypotheses need not be limited to routine maintenance or testing,either. A hypothesis could be defined along the lines of “Thistransformer should be replaced” and rules added to apply various piecesof information or evidence that may favor or disfavor this hypothesis.

There are, however, a couple of caveats. First, the assessment is onlyas good as the information provided by the user and the expertknowledge. For example, if the rules do not consider transformer cost orcriticality in a replacement decision, then obviously the resultingbelief would not account for these factors. (As an aside, thishighlights the need for a transparent rule base.) In addition, thebelief or certainty factor only indicates the amount of evidencesupporting a replacement decision, not remaining service life. Inassessing decisions in this fashion, there is a significant disadvantagein that the utility considerations are not explicit, but are buried inthe certainty factors.

Before discussing the system and method approaches in detail, it isappropriate to first discuss some of the desired characteristics andrequirements for a successful transformer fleet decision support system.These requirements shall guide the selection of the assessmentmethodologies.

Operate with a minimum of data: With the advancing age of mosttransformer fleets, the utilization of this readily available data inmaking asset management decisions based on unit condition is essential.However, the amount of data involved is quite significant. Dissolved GasAnalysis (DGA) data alone can account for tens of thousands of testrecords for fleets as small as one hundred units. The use of an expertsystem provides an efficient means for analyzing the readily availabledata in order to distinguish units that may be candidates for additionaltesting and investigation. The asset manager's field of view can then beshifted from the large fleet to a small subset of suspect units.

Example rules for dissolved gas analysis (DGA) using the system areshown below. The numbers in the square brackets specify the “good” and“bad” limits respectively. For example, the term “H2 is High[100, 1000]”specifies a belief of −1.0 for H2 values less than 100 ppm, a +1.0belief for H2 values above 1,000 ppm, and beliefs interpolated between−1.0 and +1.0 for H2 values between 100 and 1,000 ppm.

IF (H2 is High [100, 1000] AND NOT (CH4 is High [0, 400] OR C2H6 is High[0, 200] OR C2H4 is High [0, 150] OR C2H2 is High[0, 10])) THEN PD (1.0)IF ((CH4 is High [0, 400] OR C2H6 is High [0, 200]) AND NOT (C2H4 isHigh [0, 150] OR C2H2 is High [0, 10])) THEN T1-T2 (1.0) IF (C2H4 isHigh [0, 150] AND NOT C2H2 is High [0, 10]) THEN T2-T3 (1.0) IF (C2H2 isHigh [0, 10] AND NOT (CH4 is High [0, 400] OR C2H6 is High [0, 200] ORC2H4 is High [0, 150])) THEN D1 (1.0) IF (C2H2 is High [0, 10] AND (CH4is High [0, 400] OR C2H6 is High [0, 200] OR C2H4 is High [0, 150]))THEN D2 (1.0)

In the expert system described herein, the minimum threshold for datawas intentionally set quite low, requiring only DGA data and somelimited nameplate data (vintage, manufacturer, MVA rating). Availabledata varies widely depending upon company maintenance philosophies andrecordkeeping efforts. Most often, DGA and oil quality data is readilyavailable in a convenient electronic format. Demographic informationsuch as transformer manufacturer, vintage, voltage, MVA rating andcooling type are available to widely varying degrees. Electrical testdata, such as insulation power factor or TTR, is generally less commonlyavailable, particularly in a format amenable to automated processing. Itis essential that a practical method for supporting power transformermaintenance and asset management decisions be capable of providing auseful result with a minimum amount of information. Obviously, someinformation must be present. Otherwise, the analysis would amount torandom guessing. In order to provide a maximum of information with aminimum of effort, the practical minimum set of data shall consist ofDGA test data. Ideally, some transformer nameplate information wouldalso be included in this minimum set, but need not be strictlynecessary.

Operate with missing data: Data quality and data set completeness variesfrom one company or population segment to the next. Even withinrelatively complete data sets, it is likely that some information willbe missing or deemed erroneous. To be practical, the assessmentmethodology must not be impeded by missing data. The methodology must becapable of providing the best evaluation of the existing evidencewithout penalizing for missing data. This requirement is essential.

Allow modification of knowledge base as knowledge evolves: The problemdomain of power transformer diagnostic is highly complex withsurprisingly little supporting data. Asset owners and operatorstypically know very little about the specific design details ofindividual power transformers. Design details vary considerably withmanufacturer, vintage and duty. Given the magnitude of the problem athand, it is impractical, and inefficient, to develop a complete andcomprehensive knowledge base. To do so would require a tremendous amountof effort and resources and ultimate success would not be guaranteed.Therefore, it is more practical to produce a knowledge base that coversthe most common scenarios. As information or experience is gained, orunusual situations are encountered, the knowledge base should allow forthe addition of new intelligence without requiring a completerefactoring of the knowledge base. Ideally, new knowledge could becaptured is an isolated and modular fashion without requiring extensivemodification of the current knowledge base. This would allow for theefficient development of an evolving knowledge base.

Transparent: In order to gain industry acceptance and provide maximumdecision support, the assessment methodology should be transparent. Theuser should be able to gain an explanation for each conclusion producedby the tool in terms of the supplied evidence. Ideally, this explanationwould be in something approximating natural language. However, it wouldbe sufficient if users were able to trace through the assessmentmethodology in terms of the underlying mathematics and algorithm withsufficient detail to trace the reasoning behind each conclusion.

Enable support for a variety of decisions: This assessment methodologywill be applied to a variety of decision-making tasks including, butcertainly not limited to, replacement and asset management decisions ormaintenance and monitoring tasks. Examples of the latter might includeadditional testing to narrow the range of possible diagnoses, increasedsurveillance of suspect units, or corrective maintenance of undesirableconditions.

Scalable to large amounts of data with varying frequency: As onlinemonitoring devices are becoming more economical and gaining industryfavor, the amount and frequency of available data is increasing. Thisadditional information can provide a wealth of diagnostic insight,provided the assessment methodology can scale to handle the increaseddata bandwidth. Online DGA monitors are particularly intriguing. Thesedevices are capable of detecting rapidly evolving failure modes. Thisdetection capability is useless, however, unless there is some means tobring the potential fault to the attention of personnel in a position tointervene before catastrophic failure occurs.

A guiding principle in the development of the system and methodology isto limit the initial data requirements to data that is readily availablein an electronic form convenient for automated analysis. The initialanalysis is then used to target more costly data gathering or testingefforts. DGA and oil quality data is often available in a format thatcan be easily exported to a spreadsheet or database. Along with somebasic nameplate information, it is that the initial screening analysisis done utilizing this readily available information.

The following is a detailed list of initial data requirements. Themethodology is designed to be applicable using whatever data isavailable, as much as possible. This is one of the key requirementsoutlined above. As a minimum, DGA data is required, along with someminimal transformer information (items marked with a “1”). Additionalinformation is strongly encouraged (items marked with a “2”). Other datawill be utilized when available to refine the analysis.

-   -   Transformer Data:    -   Serial Number (1)    -   Manufacturer (1)    -   Year of Manufacture (1)    -   Oil Preservation System (2)    -   Transformer or Autotransformer    -   Core or Shell type    -   Temperature Rating (55 C, 65 C, 55/65 C)    -   Winding Voltages/Connections    -   MVA Ratings (2)    -   Cooling Mode Types (2)    -   Is there an LTC? (2)    -   Manufacturer    -   Model    -   Oil Preservation Type on LTC Compartment    -   Is there a DETC?

DGA Data (1):

-   -   Serial Number (or some other unique equipment identifier)    -   Sample Date    -   Compartment (if data includes LTC DGA or DGA from PST)    -   H₂    -   CH₄    -   C₂H₆    -   C₂H₄    -   C₂H₂    -   CO    -   CO₂    -   O₂    -   N₂

Oil Quality Data:

-   -   Serial Number    -   Sample Date    -   Oil Temperature during Sampling    -   Moisture    -   D1816    -   IFT    -   Acidity    -   Color    -   Furans

Dissolved Gas Analysis (DGA) is one of the most useful and most widelyused power transformer condition assessment techniques. This techniqueis sensitive to a wide range of malfunctions, both thermal andelectrical, which could eventually lead to failure of a transformer ifcorrective measures are not taken.

Sampling intervals are typically from 1 to 3 years depending on the sizeand voltage of the transformer; with more frequent sampling for large,critical units and less frequent sampling for smaller, less criticalunits. On more critical units, or units exhibiting signs of a potentialevolving fault, online gas monitors may be utilized that provide DGAmeasurements at frequent intervals.

Referring to FIG. 7, overstressed insulation in oil filled transformersproduces various gasses which are absorbed into the oil. The quantityand composition of these gasses depends partly on the kind of insulationand the temperature. By extracting the gasses from the oil and analyzingthem it is possible to diagnose the kind of failure which produced themand where the damage is occurring.

Dissolved gas analysis is the procedure by which the gasses areanalyzed. The type of gasses and their concentration in the oil are usedto identify problems in a transformer before a failure occurs. Thistechnique can be used to identify problems in any part of a transformerthat is in contact with the oil (or allows communication of the gases tocomponents in contact with the oil). The test, however, is not specificfor the exact location and cause of the fault.

Different gases are produced by the decomposition of oil at differenttemperatures. Hydrogen is generated with fairly low energy faults, suchas partial discharge, and temperature as low as 150° C. In thistemperature range (150° C.-200° C.), methane is also produced. Beginningat approximately 200° C., Ethane is generated. At still highertemperatures in excess of 300° C., Ethylene is produced. Under thehighest temperatures, generally associated with arcing conditions,Acetylene is produced. It is important to keep in mind that high energyfaults will produce a gradient of temperatures around the faultlocation, generating the lower-energy fault gases in some quantity. Byexamining the quantity and type of gases present, it is possible toassess the general type of fault present, and to some degree themagnitude of fault.

DGA operates on the principle that certain gases are generated as oildegrades at certain temperatures or energies. For examples, Ethylene isgenerally generated at temperatures in excess of 700° C., well abovenormal transformer operating temperatures. Acetylene (C₂H₂) is usuallygenerated only at temperatures that are seen when an arc passes throughthe oil. It is usually best to begin with the gases that are generatedat the highest energies.

High-intensity electrical discharges, or electrical arcs, produce veryhigh temperature (over 600° C. to 700° C.), which causes the generationof small but significant quantities of acetylene. Acetylene is absentfor other types of faults, so it is a reason for major concern when itis detected. In a large, high current arc, there will be a distributionof temperatures in the oil around the arc, so we expect to see somelevels of the other heating gases. Generally, 5-10 ppm of Acetylene isenough to raise alarm and 35 ppm or higher requires immediate action.

Lower, steadily increasing levels of Acetylene can be produced by small,intermittent sparking seen when circulating currents are interrupted,e.g. core bolt sparking, sparking between core laminations, or from afailing pump motor. Acetylene can also be generated in significantquantities if there is heavy partial discharge taking place.

In the absence of Acetylene, we look next at Ethylene. High Ethylene(above roughly 100 pm) or a significant lasting trend in Ethyleneindicates temperatures above 700° C. This means something in thetransformer is getting excessively hot. This can be a bad joint, a coreproblem or occasionally, some broken turns strands. One thing to checkis the trend in CO. If CO is trending upwards, this indicates that theheating is located somewhere in contact with paper. CO/CO₂ ratio canalso be used to assess paper involvement, but this is often lessreliable (CO₂ often varies quite a bit from sample to sample and iseasily introduced by air bubbles in the sample).

Methane and Ethane are the other key heating gases. They are generatedat temperatures roughly below 300° C. High levels of these gases canindicate some high localized heating, but may also be indicative of atransformer that is simply heavily loaded.

Again, CO can be utilized to assess paper involvement. Here, levelsabove 150 ppm raise caution and levels above roughly 250 ppm raisealarm. Also, look for a sharp increase in these levels, as thisindicates that something changed. Trend is often more important thanlevel here.

Low-intensity electrical discharges in oil, sometimes referred to ascorona, produce principally hydrogen, with some methane, but lesserquantities of the other hydrocarbon gases.

Hydrogen is a tricky gas to interpret. Steadily increasing Hydrogenlevels in the absence of any other gases generally indicates somepartial discharge activity. However, this is not terribly reliable.There are some cases that are clear examples of Hydrogen produced bypartial discharge, with Hydrogen in the hundreds and low levels of othergases.

There are some other potential, though rare, sources for Hydrogengeneration in the absence of heating gases. Hydrogen can occasionally begenerated by oxidation of the tank steel if there is free water present.Some also claim the “thin film” heating of oil in between corelaminations can generate hydrogen at core temperatures in excess of 120°C.

Carbon monoxide and carbon dioxide (CO and CO₂). CO and CO₂ are the keygases produced by thermal degradation of the paper, and are often usedas indicators of paper involvement in a fault. Thermal decomposition ofcellulose, even at normal operating temperatures, produces these carbonoxide gases. Thus, low rates of production are not a cause for alarm.However, production of such gases at an abnormally high rate isassociated with overheated insulation. Both the rate of production andthe ratio of the two gases can be indicative of the severity of theoverheating.

If a fault involves the cellulose insulation, or is near enough tocellulose insulation to sufficiently heat the paper, degradation of thepaper will occur via hydrolysis, oxidation or pyrolysis. If the fault isa low temperature thermal fault (<150° C.), then hydrolysis andoxidation will be the dominant mechanisms. This is consistent withthermal aging due to normal operation. However, above roughly 150° C.,pyrolysis becomes more dominant, producing more CO. As a general rule ofthumb, the CO₂/CO ratio should be between 3 and 10. Outside of thisrange, excessive degradation of the cellulose is likely. Ratios closerto 1, with higher rates of CO generation, are indicative of pyrolysisand, therefore, excessive temperatures. Examples of extreme overheatingfrom loss of cooling have produced ratios in the 2-3 range. (This ratioshould be based on relative ppm of gas generated in a given time period,rather than total gas content, to more accurately assess the latesttemperature condition.)

Caution must be exercised when interpreting CO and CO₂ values and theratio of CO₂/CO. CO₂ results can easily be skewed by the exposure toair, either within the transformer or by accidentally drawing an airbubble with the sample. This would skew the ratio toward higher numbers.In addition, CO and CO₂ can be generated from other sources, such asdegradation of paints, coatings, adhesives and the oil, albeit in lowerquantities.

As an example, a transformer with CO₂ level at 12000 ppm and CO at 870ppm had the diagnosis of imminent risk of failure confirmed by teardownwhen grossly overheated insulation was found at the line end of the HVwinding due to a manufacturing defect which restricted cooling in alimited area.

Note that in this example the CO₂/CO ratio was not abnormal, so bothcriteria should be investigated. An unusually low CO₂/CO ratio withsmall amounts of gas present could be indicative of a developing problemthat could be corrected. The absolute quantities of gas should at leastbe in the Condition 2 status before any detailed investigation wasundertaken.

Investigation would include frequent measurement of gas-in-oil toestablish the generation rate. Since all normally operating transformerswill have some levels of the above mentioned gases dissolved in the oils(generally with the exception of acetylene), it is not a simple task toidentify a definitive threshold for each gas. There are two generaltasks in DGA analysis: 1) fault detection and 2) fault classification.Obviously, if there is no fault present, then there is no fault toclassify. Many artificial intelligence applications in transformer DGAhave focused on fault classification. This isn't terribly useful, sincethe most difficult determination to make is whether or not a fault ispresent to begin with.

Unfortunately, there are no simple rules or thresholds that can be givenfor precisely and definitively determining the presence of a fault.There is a great deal of uncertainty that makes the process more “art”than science. Part of the difficulty lies in the interrelationship ofthe various gases. Thresholds for concern are often expressed as limitson the individual gases without regard for the levels of other gases.This is can be a misleading approach, however.

While a certain gas level might be concerning in the absence of othergases, in conjunction with other gases this same gas level mightindicate a less concerning condition. For example, generation ofHydrogen alone might indicate partial discharge activity, a seriouscondition. However, in conjunction with some of the heating gasses(Methane, Ethane), the same Hydrogen levels might indicate a heavilyloaded unit.

Knowledge of the transformer design and its history in service is alsovery important in the interpretation process. For example, certainWestinghouse shell form transformers had a stray flux heating problemwith the tee-beam that supports the windings. These units will generateexcessive quantities of the heating gases (Methane, Ethane and Ethylene)and even some Acetylene. Without taking into consideration thisparticular design issue, these gas levels would be highly alarming.However, experience has shown that these units can continue to operatewhile generating these high levels of gases.

In other cases, similar patterns of gassing could be indicators ofevolving incipient faults in the windings, such as the evolution from asmall strand-to-strand fault into a disastrous turn-to-turn orsection-to-section fault. This type of knowledge, combined with thegas-in-oil history, is essential in the decision-making process fordeciding the appropriate course of action once a potential problem hasbeen identified. Therefore, a successful DGA interpretation algorithmmust include the flexibility to specify knowledge specific tomanufacturers, vintages or any other unique category of transformers.See FIG. 6.

It is important to remember that DGA is not a “specific” test. DGA willgenerally not point to specific failure mechanism, but rather onlygenerally describe the energy involved and perhaps whether the paperinsulation is involved. Therefore, the algorithms and knowledge basewill be structured to classify faults only to the extent that DGAanalysis is capable of discerning between categories of faults. Thisfault classification can then be combined with other information, suchas oil quality, nameplate data or electrical test data to attempt toascertain a more specific failure mechanism. By structuring theknowledge base in this fashion, the number of rules can be reduce, butmore importantly it is assured that the diagnostic value of DGA is notoverstated and that any differentiation between failure mechanisms iswarranted by the evidence and not an artifact of the knowledgeimplementation.

Referring to FIGS. 4 and 5, below is a list of general fault typescapable of being discerned by DGA. This reflects a reasonable level ofgranularity. The rules in the analysis engine will be structured toindicate the presence of faults in these categories. Additionalinformation will then be utilized to differentiate between possiblefailure mechanisms. Examples of failure mechanisms associated with eachfault classification are listed below, as well. These lists are by nomeans exhaustive.

PD—Partial Discharge: Characterized by generation of Hydrogen, with lowlevels of Methane and traces of Ethane and Ethylene. May produce CO andCO₂ if cellulose insulation is involved. Severe partial discharge cangenerate Acetylene.

-   -   Voids in insulation due to improper impregnation    -   Contamination from moisture    -   Contamination from particulates

D1—Low-level Arcing: Characterized by generation of Acetylene, with lowlevels of Ethylene and other “heating” gases.

-   -   Tracking due to contamination or improper design    -   Insulation puncture or flashover with little power follow        through.    -   Loose core ground    -   Intermittent unintended core ground    -   Strand-to-strand fault

D2—High-Level Arcing

-   -   Insulation flashover with power follow through.    -   Due to a variety of causes including poor design, overvoltage,        short-circuit damage, contamination.

T1—Temperatures <300° C.

-   -   Normal aging    -   Planned overloading    -   Stray Flux Heating (Tank, Support Structure)    -   Circulating currents in core due to multiple core grounds    -   Winding circulating currents    -   Bad Joint    -   Coked contacts    -   Blocked oil ducts    -   Clogged coolers    -   Blocked radiator    -   Lead Heating

T2—Temperatures >300° C., <700° C.

-   -   Stray Flux Heating    -   Bad Joint, accelerating to failure    -   Coked contacts

T3—Temperatures >700° C.

-   -   Severe Stray Flux Heating    -   Bad Joint—Critical    -   Bad De-Energized Tap Changer Contact

Trends in gases are commonly overlooked in DGA analysis methodologies.The trends in the gas levels are often more enlightening than theabsolute gas levels. The trend can provide some indication of theseverity of the fault and whether the fault is active. However, trendspose a great deal of difficulty in formulating automated analyticaltools. There is a significant amount of variability in the gas levels,some more so than others. For example, Hydrogen and Methane areparticularly variable gases, while the more soluble gases like Ethyleneor Acetylene tend to be more stable from one sample to the next. Giventhe infrequent sampling intervals, there are few data points availableto establish a clear trend.

The utility in examining the trends is indisputable. However, discerningbetween trends due to active gassing and trends that are simply due toanalytical error or changes in gas solubility is difficult, even for atrained observer.

The system and method produces indices designed to provide a indicationof a transformer's condition in respect to a defined set of internalfailure mechanisms. Transformers can fail in innumerable ways. Failuremechanisms are a rough classification of a condition or defect that mayresult in failure of the transformer. Failure mechanisms, as set forthhere, roughly group specific failure scenarios into categories that havesimilar root causes, fault evolution characteristics and incrementaloperating risk. Presently, there are 23 different failure mechanismsthat have been defined. They are:

-   -   Moisture Ingress    -   Particle Contamination    -   Oxygen Ingress    -   Active Thermal Degradation    -   Winding Local Overheating—Bad Joints    -   Winding Local Overheating—Eddy Losses    -   Winding Local Overheating—Circulating Currents    -   Winding Local Overheating—Magnetic Saturation    -   Lead Heating    -   Winding—Mechanical Damage    -   Winding—Tracking/PD    -   Winding—Shorted Turn Strands    -   Winding—Dielectric Failure    -   Ungrounded Metallic Part    -   Unintended Core Ground    -   Core Bolt Insulation Shorting (specific types)    -   Loose Core Laminations    -   Core Shorts—Edge Burrs    -   Core Heating—Overexcitation    -   Core Heating—Circulating Currents    -   Core Heating—Unbalanced Flux    -   Core Heating—DC in Neutral    -   Core Face Overheating

It is natural for subject matter experts to express “beliefs” or thedegree to which a given piece of evidence confirms or disconfirms ahypothesis or conclusion. Beliefs allows for quantification of thesubjective opinions of subject matter experts and consequently, theindices can be considered as measures of the belief in a particularcondition.

Evidence, in the form of test data and nameplate information, isevaluated to determine a “belief” that a given failure mechanism islikely. The belief factors allow for reasoning under the uncertaintythat often accompanies substation equipment assessments.

Given sparse information, it is often not possible to specificallypinpoint a single failure mechanism or differentiate between multiplefailure mechanisms. Quite often, several potential failure mechanismsare identified concurrently.

As stated above, there are 23 different failure mechanisms, each withit's own belief. If each of the failure mechanisms were examined as thehigh level output, there would be 23 numbers for each transformer. Tosummarize the output in a meaningful, yet succinct fashion, four indiceswere developed. These indices aggregate the beliefs based on the broadphysical mode of the individual failure mechanism. The value of thesehighest level indices is meant to provide a measure of the belief thatone or more of the constituent failure mechanisms exists. These indicesare comprised of normal degradation and abnormal condition components.The development of normal degradation index to assist with transformerreplacement decisions is unique to this development and a majorbreakthrough.

For example a measure of 1.0 in the Abnormal Core index indicates a highmeasure of belief that a core failure mechanism exists. Correspondingly,a measure of 0 in the Abnormal Core index would indicate with a highbelief that a core failure mechanism does not exist.

Normal Degradation Index

This index is intended to provide an indication of the physicalcondition of the paper insulating system relative to its initial state.Transformers undergo normal aging or degradation due to operation of thetransformer under conditions that do not exceed the design criteria ofthe transformer. This normal degradation is generally due to aging ofthe paper insulation system, in which the paper insulation experiencesdecreasing mechanical strength as a function of time and temperature.

Paper ages at any temperature, however the aging rate is greatlyincreased as temperature increases. Paper degradation can occur due tohydrolysis, oxidation or pyrolysis, whereby heat, moisture and oxygenare the primary catalysts. These processes break bonds in the cellulosechains, decreasing mechanical strength. Normal degradation is generallya slow process.

Units that have elevated Normal Degradation Indices are not expected toexperience a rapid deterioration in condition in the near term. A highnormal aging index does not necessarily mean that the transformer isclose to failure but rather that it should be a more likely candidatefor retirement considerations going forward. This index is primarilyuseful for fleet and asset management, budgeting and replacementplanning. Currently, a Normal Degradation Index of greater than 0.25indicates a unit that warrants further scrutiny. Experience indicatesthat Normal Degradation Index values above 0.60 highly correlate withunits that have insulating paper that is no longer capable of providingreliable service.

With the minimum amount of information, DGA and vintage, there arelimitations to assessing the condition of the paper. Certain gaspatterns (high methane, little to no ethylene) suggest a heavily loaded,but normally operating transformer. CO levels and trends are also highlysuggestive of degradation, as CO is only generated in a transformer bydecomposition of the cellulose chains in the insulating paper. Thisinformation combines to generate a belief in whether the recent historyindicates that there was active thermal degradation for the precedingtime period.

The normal degradation index is calculated is as follows:

-   -   1. A belief in “active thermal degradation” is calculated at        each point in time based upon available evidence (strictly gases        at this step).    -   2. The belief at each point in time is multiplied by the time        interval between the present sample and the previous sample and        added to a running sum (simple integration of the “active        thermal degradation” over time)    -   3. This integrated belief is divided by the number of days in        the gas history to give an average increment per day.    -   4. The average increment is multiplied by the number of days in        service to give a number representative of the total aging        projected over the in-service history of the transformer.        Obviously, a lengthy service history will minimize the length of        the projection, while providing a greater wealth of history to        form a better average rate of degradation.    -   5. To derive an index from this equivalent age indicator, total        aging indicator is normalized over an average life expectancy of        30 years. The magnitude of this average life expectancy is not        critical, as thresholds for action are derived through field        experience and measurement of degree of polymerization.

Abnormal Thermal, Electrical and Core Indices

This index is used to identify units that may be experiencing a varietyof unexpected problems due to manufacturing or operating issues ordefects. Transformers in these categories show the existence of somecondition that would not be present or expected in normal operation.This could be excessive temperatures that don't fit the pattern of gasseen with normally operating, heavily loaded transformers, or it couldbe some indication of partial discharge, arcing or sparking. Units withheating gases and no indication of paper involvement may also show up inthe “abnormal core” category. The important difference, however, is thatthis index is NOT a function of service age.

While some vintage-specific type issues may be involved, age does notincrease or decrease an abnormal index. These conditions can occur atany point during the service life of a transformer. A high abnormalindex value indicates a need to take more immediate action e.g.additional tests or monitoring or inspection. This index indicatestransformers that may or may not evolve to failure in the near future.Time to failure is highly variable and dependent on the specifics of anypotential underlying fault condition. Higher Abnormal Condition Indicesidentify transformers that should be reviewed in further detail byappropriate personnel.

Abnormal Condition Indices are divided into three categories: Thermal,Electrical, and Core. Note that due to the non-specific nature of fielddiagnostic tests, a single defect may provide indications in more thanone category. Any Abnormal Condition Index Value above 0.5 warrantsfurther review.

The following is a list of failure mechanisms that contribute to each ofthe three Abnormal Condition Indices:

Abnormal Thermal

-   -   Contributors:        -   Winding Local Overheating—Bad Joints        -   Winding Local Overheating—Eddy Losses        -   Winding Local Overheating—Circulating Currents        -   Winding Local Overheating—Magnetic Saturation        -   Lead Heating

Abnormal Electrical

-   -   Contributors:        -   Winding—Shorted Turn Strands        -   Winding—Dielectric Failure        -   Winding—Tracking/PD        -   Ungrounded Metallic Part        -   Static Electrification

Abnormal Core

-   -   Contributors:    -   Unintended Core Grounds        -   Ungrounded Metallic Part        -   Core Bolt Insulation Shorting        -   Loose Core Laminations        -   Core Overexcitation        -   Core Circulating Currents        -   Core Face Heating        -   Core Heating—Unbalanced Flux        -   Core Shorts—Edge Burrs        -   Core Heating—DC in Neutral

In addition to the four indices above, an oil quality index has beenincorporated into the system. The oil quality in transformers isimportant for the following reasons:

-   -   Maintaining the dielectric strength of the oil in EHV and UHV        transformers has long been recognized as critical. This is        intuitive.    -   The importance of this is increasing as design margins are        reduced to nil.    -   Newer research has highlighted the role of moisture, oxygen and        acidity of the oil in reducing the longevity of the paper        insulation system.    -   Deposition of polar contaminants on insulation surfaces is a        concern with heavily degraded oil

The oil quality index is used in conjunction with the four previouslydescribed inidices to provide a more complete picture of transformerhealth. The system and method allows thresholds, which do not have crispparameters, to be used. While the thresholds are an important startingplace, data tables do not recognize the relative importance of sometests over others. For example, why use one set of thresholds overanother set. The following rules can apply in the oil quality index.

-   -   If dielectric strength is bad, action must be taken    -   If IFT is low, action can wait depending on how low it is and        the voltage class of the transformer    -   If several parameters are out of bounds, problem is greater and        time to action is less

More Than Just an Index

-   -   Oil contamination and degradation can take a variety    -   Oil remediation presents a series of possible corrective actions        based on which oil quality parameters are insufficient.    -   Presenting a single index value, while useful as a gauge for the        severity of an oil quality problem within a transformer and        across a fleet does not suggest the appropriate remedy    -   A more complex tree of “Actions” must be developed to give the        user specific guidance:        -   Do nothing        -   Process with vacuum degasser, moisture removal and paper            filter        -   Treatment with Fuller's Earth        -   Replace the oil

The method for evaluating a transformer is described generally below.The method allows a user to evaluate specific transformers based on datagathered and the rules-based system to determine what type of actionshould be performed. As shown in FIG. 9, a user first gathers readilyavailable data for inputting into the input module 11 of the system 10(block 20). The intent of the system and method is to make use of datathat is readily available, i.e., obtainable with a minimal amount ofeffort. For power transformers, this data includes:

-   -   Nameplate data (manufacturer, vintage, etc.)    -   Dissolved Gas Analysis History (DGA), specifically H₂, CH₄,        C₂H₆, C₂H₄, C₂H₂, CO, CO₂, O₂ and N₂ taken at several dates    -   Oil Quality History (Interfacial Tension, Acidity, Dielectric        Breakdown Voltage, Moisture and Color)    -   Furans (2-Furfuraldehyde)

Once the data has been inputted into the input module 11, data from theinput module 11 is accessed by the analysis engine module 12. Theanalysis engine module 12 uses rules to analyze the data. First, theanalysis engine module 12 infers broad physical conditions from the data(block 21). More particularly, the data, especially the oil test data(DGA and oil quality) provides indications of broad physical conditionsthat might occur in the bulk of the transformer oil. These conditionsinclude:

-   -   PD—Partial Discharge: Characterized by generation of Hydrogen,        with low levels of Methane and traces of Ethane and Ethylene.        May produce CO and CO2 if cellulose insulation is involved.        Severe partial discharge can generate Acetylene.

Example Rule: IF Transformer.DGA.blfH2_High AND NOT(Transformer.DGA.blfCH4_High OR Transformer.DGA.blfC2H6_High ORTransformer.DGA.blfC2H4_High OR Transformer.DGA.blfC2H2_High) THENTransformer.DGA.blfLightPD (1.0) ELSE !Transformer.DGA.blfLightPD (1.0)END

-   -   D1—Low-level Arcing: Characterized by generation of Acetylene,        with low levels of Ethylene and other “heating” gases (Ethane,        Methane).

Example Rule: IF Transformer.DGA.blfC2H2_High AND NOT(Transformer.DGA.blfCH4_High OR Transformer.DGA.blfC2H6_High ORTransformer.DGA.blfC2H4_High) THEN Transformer.DGA.blfSparking (1.0)ELSE !Transformer.DGA.blfSparking (1.0) END

-   -   D2—High-Level Arcing: Characterized by generation of Acetylene        with higher levels of Ethylene and other “heating” gases.

Example Rule: IF Transformer.DGA.blfC2H2_High AND(Transformer.DGA.blfCH4_High OR Transformer.DGA.blfC2H6_High ORTransformer.DGA.blfC2H4_High) THEN Transformer.DGA.blfArcing (1.0) ELSE!Transformer.DGA.blfArcing (1.0) END

-   -   T1—Temperatures <300° C.: Characterized by generation of mostly        methane with low levels of Ethane and at most traces of        Ethylene. No Acetylene.

Example Rule: IF (Transformer.DGA.blfCH4_Moderate ORTransformer.DGA.blfC2H6_Moderate) AND NOT (Transformer.DGA.blfC2H4_HighOR Transformer.DGA.blfC2H2_High) THEN Transformer.DGA.blfLowHeat (1.0)ELSE !Transformer.DGA.blfLowHeat (1.0) END

-   -   T2—Temperatures >300° C., <700° C.: Characterized by generation        of Methane and Ethane with potential of low amounts of Ethylene.        No Acetylene.

Example Rule: IF Transformer.DGA.blfCH4_High AND NOT(Transformer.DGA.blfC2H4_High OR Transformer.DGA.blfC2H2_High) THENTransformer.DGA.blfModerateHeat (1.0) ELSE!Transformer.DGA.blfModerateHeat (1.0) END

-   -   T3—Temperatures >700° C.: High amounts of Ethylene with other        “heating gases”. No Acetylene.

Example Rule: IF Transformer.DGA.blfC2H4_High AND NOTTransformer.DGA.blfC2H2_VeryHigh THEN Transformer.DGA.blfHighHeat (1.0)ELSE !Transformer.DGA.blfHighHeat (1.0) END

In addition to the conditions listed above, an assessment of paperinvolvement from CO, CO₂ and CO₂/CO ratio (and trends thereof) isperformed. Increasing amounts of CO indicate the potential of activedegradation of the paper insulation and can help localize the fault tothe current carrying circuit. As a general rule of thumb, the CO₂/COratio should be between 3 and 10. Outside of this range, excessivedegradation of the cellulose is likely. Ratios closer to 1, with higherrates of CO generation, are indicative of pyrolysis and, therefore,excessive temperatures.

With the broad physical conditions and the assessment of paperinvolvement complete (block 21), the analysis engine module 12 takes oneof two tracks. The first track is to determine abnormal indices (block23) and the second track is to determine a normal degradation index(block 26). With respect to the first track, beliefs are assigned orcalculated to individual failure mechanisms based on the broad physicalconditions and paper involvement (block 22). It is these failuremechanisms that drive the abnormal indices in block 23. For example,belief in Winding Bad Joints is calculated from the following rules:

IF Transformer.DGA.blfHighHeatPaper THEN Transformer.fmWdgBadJoints(0.6) END IF Transformer.DGA.blfHighHeatPaperTrend THENTransformer.fmWdgBadJoints (0.8) END IFTransformer.DGA.blfHighHeatNoPaper THEN Transformer.fmWdgBadJoints (0.6)END IF Transformer.DGA.blfHighHeatNoPaperTrend THENTransformer.fmWdgBadJoints (0.8) END IF Transformer.DGA.blfArcing THENTransformer.fmWdgBadJoints (0.1) END IF Transformer.DGA.blfArcingTrendTHEN Transformer.fmWdgBadJoints (0.2) ELSE !Transformer.fmWdgBadJoints(0.1) END IF Transformer.DGA.blfSparking THEN Transformer.fmWdgBadJoints(0.4) END IF Transformer.DGA.blfSparkingTrend THENTransformer.fmWdgBadJoints (0.6) END

For each abnormal index, a pre-determined set of failure mechanisms isused to calculate the indices as a maximum belief in. The pre-determinedfailure mechanisms are listed below by index.

-   -   Abnormal Thermal Index:        -   Winding Local Overheating—Bad Joints        -   Winding Local Overheating—Eddy Losses        -   Winding Local Overheating—Circulating Currents        -   Winding Local Overheating—Magnetic Saturation        -   Lead Heating    -   Abnormal Electrical Index:        -   Winding—Shorted Turn Strands        -   Winding—Dielectric Failure        -   Winding—Tracking/PD        -   Ungrounded Metallic Part        -   Static Electrification    -   Abnormal Core Index:        -   Unintended Core Grounds        -   Ungrounded Metallic Part        -   Core Bolt Insulation Shorting        -   Loose Core Laminations        -   Core Overexcitation        -   Core Circulating Currents        -   Core Face Heating        -   Core Heating—Unbalanced Flux        -   Core Shorts—Edge Burrs        -   Core Heating—DC in Neutral

With the beliefs for the failure mechanisms in each index determinedusing the process described above, the maximum belief calculated for thegroup is used to determine the abnormal index. For example, if “BadJoints” is the highest calculated belief for the abnormal thermal index,then the abnormal thermal index is equal to that belief. With theabnormal indices determined, the output module 13 outputs the resultsfor the user to review. If any of the Abnormal Indices (Thermal,Electrical, or Core) exceeds 0.5, then an investigation for potentialincipient fault is needed and corrective action is taken if necessary(block 24). Example actions include (1) changing oil sampling frequency,(2) installing online monitoring, (3) electrical testing, and (4)performing an outage and conducting internal inspections.

For track two, the broad physical conditions and the paper involvementare used to calculate beliefs in active thermal degradation (block 25).An example rule is as follows:

IF Transformer.DGA.blfPaperTrend THENTransformer.fmActiveThermalDegradation (0.90) ELSE!Transformer.fmActiveThermalDegradation (0.90) END

Once the active thermal degradation belief is determined, the normaldegradation index is calculated (block 26) using the following steps:

-   -   Multiply belief in active thermal degradation by the time        interval between the present test date and the previous test        date and add to a running sum (Integrated Normal Degradation        Increment). All of the above described steps are repeated for        each test point in history, chronologically ordered.    -   The Integrated Normal Degradation Increment is divided by the        number of days in the test history to give an average increment        per day.    -   The average increment is multiplied by the number of days in        service to give a number representative of the total aging        projected over the in-service history of the transformer.        Obviously, a lengthy service history will minimize the length of        the projection, while providing a greater wealth of history to        form a better average rate of degradation.    -   The Normal Degradation Index is then determined by normalizing        the total ageing indicator over an average life expectancy of 30        years. The magnitude of this average life expectancy is not        critical, as thresholds for action are derived through field        experience and measurement of degree of polymerization.

The output module 13 provides a user with the calculated normaldegradation index. If the normal degradation index is greater than 0.25then an indication of potential degradation exists and furtherinvestigation for replacement is warranted. At this point, furananalysis would be performed, as well as any additional offline analysisto support or prioritize replacement decisions (block 27).

In addition to the first and second tracks, an oil index (block 28) isdetermined from the readily available data (block 20). The oil index isused to determine when to process the oil in a transformer (block 29).See FIGS. 10 and 11. The following example rules are used by theanalysis engine module 12 to calculate the oil index from oil qualitytest data:

Example Rules: IF Transformer.Description.HV_kV >= 230 ANDTransformer.OilQuality.Acidity == HIGH(0.08, 0.12) THENTransformer.OilQuality.blfHighAcidity(1.0) END IFTransformer.Description.HV_kV >= 230 AND Transformer.OilQuality.Acidity== LOW(0.08, 0.12) THEN !Transformer.OilQuality.blfHighAcidity(1.0) ENDIF Transformer.OilQuality.blfLowDielectric_D1816_1mm THENTransformer.fmOilDegradation (0.9) ELSE !Transformer.fmOilDegradation(0.7) END

With the oil index calculated, an action by a user is determined(process oil, process oil with 5mic paper filter, process oil withFuller's Earth, investigate, and continue monitoring). An example ruleincludes:

IF Transformer.DGA.blfC2H2_Trend_High THENTransformer.actionWeeklyOilSampling(1.0) END

Analysis of various transformers was performed using the above describedsystem and method. The results are shown below.

EXAMPLES Example 1

-   -   345/118/34.5 kV 672MVA FOA 1995 North American Transformer    -   Dislodged magnetic shunts    -   Repaired on site Oct. 26, 2007    -   Date of last oil sample used in analysis: 10/25/2007    -   Normal Degradation Index=0.09    -   Abnormal Thermal Index=0.28    -   Abnormal Electrical Index=0.92    -   Abnormal Core Index=0.46    -   Flagged Abnormal by System

Example 2

-   -   345/230/13.2 kV 336/448/560MVA FOA 1991 ABB    -   Inadequate core cooling, shifted laminations    -   Removed from service late 2007. Rewound 2008.    -   Date of last oil sample used in analysis: 8/20/2007    -   Normal Degradation Index=0.73    -   Abnormal Thermal Index=0.57    -   Abnormal Electrical Index=0.72    -   Abnormal Core=0.46    -   Flagged Abnormal by System

Example 3

-   -   112.8/14 kV 62.5MVA FOA 1952 General Electric    -   Failed Sep. 7, 2012. No additional information given.    -   Date of last oil sample used in analysis: 12/2/2010    -   Normal Degradation Index=0.63    -   Abnormal Thermal Index=0.10    -   Abnormal Electrical Index=0.00    -   Abnormal Core Index=0.00    -   NOT flagged abnormal by System    -   No anomalous test data prior to failure    -   Last DGA sample 2 years prior to failure

Example 4

-   -   115/13.8 kV 25MVA FOA 1977 McGraw Edison    -   Failed Nov. 14, 2003. Lead failure during throughfault.    -   Date of last oil sample used in analysis: 10/10/2003    -   Normal Degradation Index=0.33    -   Abnormal Thermal Index=0.07    -   Abnormal Electrical Index=0.07    -   Abnormal Core Index=0.00    -   NOT Flagged abnormal by System    -   High trend in H2 between March 2003 & July 2003. This pattern is        not uncommon, as H2 tends to be easily produced.    -   It appears that the failed lead was in a weakened state and        failed while carrying fault current.

Example 5

-   -   S/N 90198-A 345/118/34.5 kV 672MVA FOA 1995 North American        Transformer    -   Dislodged magnetic shunts    -   Repaired on site Oct. 26, 2007    -   Date of last oil sample used in analysis: 10/25/2007    -   Normal Degradation Index=0.09    -   Abnormal Thermal Index=0.28    -   Abnormal Electrical Index=0.92    -   Abnormal Core Index=0.46    -   Flagged Abnormal by System

Example 6

-   -   S/N AEM30712 345/230/13.2 kV 336/448/560MVA FOA 1991 ABB    -   Inadequate core cooling, shifted laminations    -   Removed from service late 2007. Rewound 2008.    -   Date of last oil sample used in analysis: 8/20/2007    -   Normal Degradation Index=0.73    -   Abnormal Thermal Index=0.57    -   Abnormal Electrical Index=0.72    -   Abnormal Core=0.46    -   Flagged Abnormal by System

Example 7

-   -   S/N 02-8226-52761-1 110/14.4 kV 56MVA FOA 1971 Allis Chalmers    -   Failed during throughfault on Jun. 7, 2010    -   Date of last oil sample used in analysis: 3/16/2010    -   Normal Degradation Index=0.65    -   Abnormal Thermal Index=0.32    -   Abnormal Electrical Index=0.05    -   Abnormal Core Index=0.15    -   NOT flagged abnormal by System    -   No anomalous test data prior to failure    -   Given that it was a throughfault failure, do not expect prior        indications. However, the high NDI suggests advanced paper        degradation, elevating risk of failure on throughfault.

Example 8

-   -   S/N 8975515 112.8/14 kV 62.5MVA FOA 1952 General Electric    -   Failed Sep. 7, 2012. No additional information given.    -   Date of last oil sample used in analysis: 12/2/2010    -   Normal Degradation Index=0.63    -   Abnormal Thermal Index=0.10    -   Abnormal Electrical Index=0.00    -   Abnormal Core Index=0.00    -   NOT flagged abnormal by System    -   No anomalous test data prior to failure    -   Last DGA sample 2 years prior to failure

Example 9

-   -   S/N C179320 116.7/14 kV 62.5MVA FOA 1954 General Electric    -   Failed Jul. 2, 2012. No additional information given.    -   Date of last oil sample used in analysis: 7/5/2011    -   Normal Degradation Index=0.09    -   Abnormal Thermal Index=0.43    -   Abnormal Electrical Index=0.56    -   Abnormal Core Index=0.34    -   Flagged abnormal by System    -   Last DGA sample approx. 1 year prior to failure

Example 10

-   -   S/N CO562951 115/13.8 kV 25MVA FOA 1977 McGraw Edison    -   Failed Nov. 14, 2003. Lead failure during throughfault.    -   Date of last oil sample used in analysis: 10/10/2003    -   Normal Degradation Index=0.33    -   Abnormal Thermal Index=0.07    -   Abnormal Electrical Index=0.07    -   Abnormal Core Index=0.00    -   NOT Flagged abnormal by System    -   High trend in H2 between March 2003 & July 2003. This pattern is        not uncommon, as H2 tends to be easily produced.    -   According to Xcel report: “It appears that the failed lead was        in a weakened state and failed while carrying fault current. It        is possible that the transformer could have remained in service        for quite some time absent a through fault on the X1 winding”

Example 11

-   -   S/N M102052 230/115 kV 65MVA FOA 1981 General Electric    -   Still operating    -   Date of last oil sample used in analysis: 4/2/2012    -   Normal Degradation Index=0.25    -   Abnormal Thermal Index=0.12    -   Abnormal Electrical Index=0.00    -   Abnormal Core Index=0.00    -   Trending low-to-moderate temperature heating gases. Likely a        heavily loaded unit, and not due to abnormal condition.

Example 12

-   -   S/N 7000257 110/13.8 kV 84?MVA FOW 1964 Westinghouse    -   Still operating    -   Date of last oil sample used in analysis: 1/13/2013    -   Normal Degradation Index=0.24    -   Abnormal Thermal Index=0.28    -   Abnormal Electrical Index=0.34    -   Abnormal Core Index=0.34    -   DGA is largely unremarkable, with some signs of active paper        degradation. Recommend furan sampling.

Example 13

-   -   S/N C255508 110/14 kV 62.5MVA FOA 1957 General Electric    -   Still operating    -   Date of last oil sample used in analysis: 1/15/2008    -   Normal Degradation Index=0.00    -   Abnormal Thermal Index=0.10    -   Abnormal Electrical Index=0.00    -   Abnormal Core Index=0.00    -   DGA is completely unremarkable, with the exception that history        ends in 2008

The foregoing has described a system and method for assessing a powertransformers. While specific embodiments of the present invention havebeen described, it will be apparent to those skilled in the art thatvarious modifications thereto can be made without departing from thespirit and scope of the invention. Accordingly, the foregoingdescription of the preferred embodiment of the invention and the bestmode for practicing the invention are provided for the purpose ofillustration only and not for the purpose of limitation.

We claim:
 1. A method for evaluating and diagnosing a condition of apower transformer contained in a power transmission system, comprisingthe steps of: (a) acquiring data representing one or more parameters ofa power transformer; (b) using rules to derive one or more broadphysical conditions of the power transformer from the acquired data; (c)using the broad physical conditions as inputs to compute a plurality ofindices, each index representing a category of failure mechanisms of thepower transformer; (d) using the plurality of indices to determine acorrective action; and (e) performing the corrective action on the powertransformer.
 2. The method according to claim 1, wherein the parametersare selected from the group consisting of nameplate data, dissolved gasanalysis history, oil quality history, and furans.
 3. The methodaccording to claim 1, wherein the broad physical conditions includepartial discharge, low-level arcing, high-level arcing, temperaturesless than 300 degrees Celsius, temperatures greater than 300 degreesCelsius and less than 700 degrees Celsius, and temperatures greater than700 degrees Celsius.
 4. The method according to claim 1, wherein thebroad physical conditions include assessment of paper involvement. 5.The method according to claim 1, wherein the plurality of indicesinclude: (a) abnormal indices selected from the group consistingessentially of abnormal thermal index, abnormal electrical index, andabnormal core index; (b) a normal degradation index; and (c) an oilquality index.
 6. The method according to claim 5, further including thestep of assigning failure mechanisms to each of the abnormal indices andassigning a belief to each of the individual failure mechanisms.
 7. Themethod according to claim 6, further including the step of determining amaximum belief assigned to a failure mechanism for each abnormal indexand assigning the maximum belief to that abnormal index.
 8. The methodaccording to claim 7, wherein if any of the abnormal indices have amaximum belief greater than 0.5, then an investigation and correctiveaction is taken.
 9. The method according to claim 5, wherein if thenormal degradation index is greater than 0.25 then an indication ofpotential degradation exists and further investigation and replacementaction is taken.
 10. A system configured to evaluate and diagnose acondition of a power transformer contained in a power transmissionsystem, comprising: (a) a computing device configured to provide a userinterface to allow a user to input data and execute rules to analyze thedata; and (b) a plurality of modules executed by the computing device,the modules being configured to conduct various stages of analysis onone or more power transformers, the modules comprising: (i) an inputmodule executed by the computing device to allow a user to input datagathered for each power transformer being analyzed; (ii) an analysisengine module executed by the computing device in response to the databeing entered into the system, the analysis engine executing pre-definedrules to determine indices; and (iii) an output module executed by thecomputing device in response to the analysis engine module determiningindices, the output module displaying results of the analysis enginemodule for a user to view and prompting the user to perform an actionrepresentative of a value assigned to the indices.
 11. The systemaccording to claim 10, wherein the analysis engine module assignsbeliefs to pre-determined power transformer failures and assignsindividual failures to a pre-determined index.
 12. The system accordingto claim 11, wherein each of the beliefs have a value between −1 and +1.13. The system according to claim 12, wherein the analysis engine moduleassigns the maximum belief for the power transformer failures containedin a pre-determined index to the pre-determined index and outputs themaximum belief to the output module for viewing by a user.
 14. Thesystem according to claim 11, wherein the pre-determined indicesinclude: (a) an abnormal index, comprising: (i) an abnormal thermalindex; (ii) an abnormal electrical index; and (iii) an abnormal coreindex; (b) a normal degradation index; and (c) an oil quality index. 15.The system according to claim 14, wherein if a maximum belief assignedto a failure mechanism for each abnormal index is greater than 0.5 thenthe computing device prompts the user to investigate and take correctiveaction.
 16. The system according to claim 14, wherein if the maximumbelief assigned to the normal degradation index is greater than 0.25then the computing device provides an indication that potentialdegradation exists and prompts the user to investigate and replace thepower transformer.